Thanks for more than two lakh views. All about openCV, Image Processing converging towards Biometric face recognition. Use the Easy Navigation button on the top bar to view all the posts at a glance related to openCV. I kept this blog small so that anyone can complete going through all posts and acquaint himself with openCV.

Pages

Friday, June 17, 2011

In my previous post, I showed you how to use your webcam to do face detection.In this post, I will explain you of how to use your webcam for eye and face detection.

Before we start, for those of you who couldn't use your webcam for face detection, I have ripped the code from previous post to make a code that takes an image as input and returns the same image with a rectangle drawn over the face as output.Here's the link for it

Here's the code for eye+face detector using your inbuilt webcam.Also note that, there is no robust eye detector related cascase file (XML file) till date.I have tested and included the most powerful one.But neverthless, if you have spectacles, remove them during testing.

Wednesday, June 15, 2011

First, let me tell you the difference between both.Face detection is just telling whether there is a face or not in the given image, on the other hand, face recognition is telling to which person that face belongs.

This is the code for face detection using your webcam.Actually, this is not exclusively my code, I hacked it from the code given by Nashruddin which inturn was hacked from the openCV samples.Any way, that's what makes the open source software better and better forever :)

Here's the code snippet

//Programme to detect faces through the webcam using the haar cascade
//Links Used:http://nashruddin.com/OpenCV_Face_Detection
#include<stdio.h>
#include<cv.h>
#include<highgui.h>
CvHaarClassifierCascade *cascade;
CvMemStorage *Membuffer;
void detectfaces(IplImage *frame)
{
int i;
CvSeq *faces=cvHaarDetectObjects(frame,cascade,Membuffer,1.1,3,0,cvSize(30,30));//Maximum 3 neighbourhood images and the last cvsize gives minimum size of face
for(i=0;i<(faces?faces->total:0);i++)
{CvRect *r=(CvRect *)cvGetSeqElem(faces,i);
cvRectangle(frame,cvPoint(r->x,r->y),cvPoint(r->x+r->width,r->y+r->height),CV_RGB(255,0,0),1,8,0);
//Draws a rectangle with two points given with color of red and thickness as 8 and line type - 1
}
cvShowImage("Video",frame);
}
int main(int argc,char *argv[])
{IplImage *frame;
CvCapture *capture;
int key;
char filename[]="haarcascade_frontalface_alt.xml";//This is the cascading file that is needed for face detector it is generally present in if openCV is your installation folder, it is present in openCV/data/haarcascades folder, don't forget to paste the haarcascade file in the same folder as your program before compiling
cascade=(CvHaarClassifierCascade *)cvLoad(filename,0,0,0);
//Loading the haar... file
Membuffer=cvCreateMemStorage(0);//Allocating the default memory for image (64K at Present)
capture=cvCaptureFromCAM(0);//Capture the image from cam with index 0
//Note that if you are using a Laptop with an external webcam it has an index 1 since the Lappie cam's index is 0
assert(cascade && Membuffer && capture);//To make sure that everything needed is always available
cvNamedWindow("Video",1);
while(key!='q')
{frame=cvQueryFrame(capture);
if(!frame)
break;
//cvFlip(frame,frame,-1);//If you are using openCV on windows, uncomment this line
frame->origin=0;
detectfaces(frame);
key=cvWaitKey(10);//Wait for 10 milli seconds, the user may press 'q'
}
cvReleaseCapture(&capture);
cvDestroyWindow("Video");
cvReleaseHaarClassifierCascade(&cascade);
cvReleaseMemStorage(&Membuffer);
}

For your convenience, I have added the comments in the code above.Don't bother if you feel lazy to copy the content.I kept the above program and the haar cascade xml file that you need to use in the link below.

I know, some of you are even lazy to, compile the code, so I have included a Linux executable in the above download link.

And this is a bonus, a youtube video of me with my face detector.I shot the video with poor lighting conditions and a VGA cam, that's why not a good one (I look 10 times more handsome), but serves the purpose

Monday, June 13, 2011

Today, let me tell you about the importance of face recognition and why is it the most sought out research in the present decade.You may have already been using the "iphoto" from Apple, which lets you name people once and then it automatically searches through your entire photo library and names them.That's face recognition.Let me tell you something more.What if it names wrong ?

Credit: Simson Garfinkel and Beth Rosenberg

Here's the greatness of Apple, it updates it mathematical model and becomes more robust.Some times it may ask you doubts also, like a small child learning to crawl :)

Google's picasa also offers, I hadn't tested it, but reviews say it is in no way the nearest competitor to Apple.You may have already heard of facebook auto tagging feature, which is a very great idea, but facebook should not have automatically enabled that feature in all accounts.And the latest news is that Google's trying to take advantage of it.It's former CEO Eric Schmidt appealed to the US government that it's a threat to national security.You may have already heard of facebook hiring a private agency to launch "article" attack on google.That's a wholly different story let's come back to our topic.

Apart from fun, actually the main reason why face recognition had to be invented and to be moved forward like a crusade is the security threats.Reports say that after the 9/11 attacks, the US government has been heavily investing on this, so that the technology can be applied in airports, railway stations and other public places to detect terrorists.But whenever the terrorist grows some beard or changes hair style or face color, it fails.Although recently more scientists claim that they have discovered the best technologies, there is no clear winner in this arena, due to the diversity in the testing procedures.Some facial recognition technologies use one image per person, while others need more than four images per person, and some need facial landmark locations accurately.In order to over come this a common facial recognition test need to be conducted using the same data for all.

This need is discovered by the Us Government early on and they have carried out these tests for every four years or so.They started the tests in 1993 and successively conducted on 1993, 1994, 1996, 2000.Recently they started a new format for testing called "Face Recognition Grand Challenge" (FRGC).The most recent one is the FRVT 2006.They may conduct another one in 2012

If your college or university wants to participate in that evaluation, you can visit their home page.Below is the link for it

I hope you are doing well with the assignments, tommorrow I will post all the code snippets for the assignments I gave you.By the end of this month you will be pursuing the research on face recognition I assure you.If you are a first timer, visiting this blog, you can view the archives in the right hand side.

Keep going forward with enthusiasm and curiosity, I have a time frame for this blog to complete the tutorial in the next 25 posts, but I may take a leave in the middle after the 30th of this month for 20 days, will try to complete it before that.I want to take you people till face recognition and leave you there for furthur research

Friday, June 10, 2011

I hope you already did the Hello World programme mentioned in the previous to previous post.For today, I will give you some assignments, try to do them.Take help of openCV documentation and the Hello World programme you had already written.Try to use the already available functions in openCV library as much as you can

Orginal Color Image

1.Make a color image into a grey scale image

Grey scale Image

2.Produce a negative of an image (Already over - helloworld)

Negative Image

3.Make a grey scale image, pure black and white (You can use the hint I mentioned in the previous post)

4.Apply patches of 4 different colors to an images

Patched Image

5.Calculate the mean, variance and standard deviation of the image

6.Create the histogram of an image, it should be printed as a seperate image

Blue Pane

Green Pane

Red Pane

Histogram is a graph, which plots the intensity of a color versus all the pixels in the image.It shows the intensity of that color at every pixel value of the image.So for an RGB image there will be three different histograms each corresponding to a different color and for grey scale only one.

If you have any doubts post them in the comments section and I am giving you three days of time to accomplish these tasks, meet you again on Tuesday

Thursday, June 9, 2011

Most of you may have worked with Adobe Photoshop or Picknik or gimp or other image processing software.Ever wondered, what is the actual thing going on there or how an image gets processed, how it is turned to blur or how it is filtered or what are the principles behind RGB, Grey Scale etc., If you are one among the few who likes to know and want to do it programmatically, it is just a applying a few functions with openCV and this is the most basic necessity for which openCV is created.For today let's discuss theory and tommorrow we will do programming.These are the standard def's of some of the main terms used in image processing

Pixel- Stands for "Picture Element".It is the basic unit of an image, just like seconds for time

Binary image - These are the oldest formats of images and most of these are oblivion, since it contains only two colors "Black" and "White" and each pixel is of just one bit, if it is 0 --> black and if it's 1-->White

Grey Scale Image - This is normally referred to as Black and white image and it's pixel consists of 8bits.If all those are zeros, then it is black and if all those are ones, then it is white and in the between values you will see the variations of colors obtained by the intermixing of black and white.

Note : And important point to note here is that although Binary images should be called Black and White, they are not called so. Hence in your Black and White TV, or any Black and White image you take, mostly you will see a variation of black and white colors, which are not exactly white nor exactly black.Remember, these are grey scale images actually.

RGB Image - This is normally referred to as color image and it's pixel consists of 24 bits, 8bits for each of Red, Blue and Green.You must have already known that the intermixing of these primary colors produces almost all the colors.If all the 24 bits are ones, you get a white image and if all are black, you get a black image

Note : Ever wondered, when people talk about 16 million colors, it's very simple, taking all the 24 bits as unit (although internally divided), we can change each bit as either 0 or 1, that gives us 2^24 or 16 million colors. Take a look at the below picture

The three pictures on the right side are black and white images, taken with red, green and blue filters, later on they are projected and combined to give a full color image.This was one of the earliest forms of taking color photographs

We normally when referring to the 8 bits, we don't use bit wise language which will be a bit confusing, and so we will be using 0 for all 0's and 256 for all 1's and between 0 and 256 for the remaining (Simply using the decimal format, instead of binary format)

Channel - Also called as component and this is the basic unit of pixel.The combination of 8bits is called a channel. So you can naturally say, that an RGB image is an 3 channelled image and Grey Scale image is an image with only one channel and generally we don't come across or discuss binary images at all, since bit wise representation is oblivion and it is generally ignored here and the rest of our journey with openCV.

Note : Although due to their bit representation, binary images are oblivion, they are not completely gone.Using heuristics you can easily note that for a grey scale image keeping a threshold (eg:127), you can convert all the pixels greater than threshold into 256 and all of them that are below to 0's, speaking in decimal language.But the actual binary representation is lost here.There is an openCV function CVthreshold(), to do this.See the documentation part you downloaded in the previous post.

This is it, take rest for today, but don't relax too much, because tommorrow it's going to be PROGRAMMING

Wednesday, June 8, 2011

Now you have crossed the most important barrier and came till here.Most people I know left the openCV during the installation itself feeling the complexity.Anyways, openCV is not for normal people :)

First of all, before getting started I want to introduce you to some links

The most important of all is the below link, which contains all the basics of getting started with openCV, try to implement and run the HelloWorld programme mentioned in the websiteIntroduction to programming with OpenCV

Next, a common metting point for all openCV users in the world, just like a common meeting point for you with your friends is openCV users yahoo group.Join it

The above two links contain all the basic functionalities that you can use with standard openCV library and their corresponding meaning and uses. While doing the hello world programme, I mentioned above, these documents can be used for reference to know, what each open CV function does

The most important of all, compiling a C++ programme written in openCV, it's one of the lengthiest and you have no need at all to understand what it implies.Consider the name of your programme is "Hell.cpp" and you want a executable "Hell", below is the code for compiling it

Tuesday, June 7, 2011

If you have no Open Source OS installed still in your system, go to my previous post and there is a link to install openCV in windows. You can skip this post.

Also if you have openCV already in your system, skip this post.

OpenCV installing is very trivial and most of the sites tell you it's easy and give you four to five commands that get the things done quickly.But if you want to really harness the complete power of openCV, follow the steps below ( For Ubuntu, can be easily applied to other Linux based OS's )

1. Although OpenCV 2.2 is the latest version, 2.1 is more stable and has got enough libraries to keep you going.

2. Follow the below link to install openCV in a step by step manner (can be applied to all Ubuntu versions)

This will automatically let you over come the ffmpeg error.Also, keep in mind, if anytime while fetching the packages through terminal, you can see, unable to fetch sometimes.Then immediately run the same command with --fix-missing as suffix and keep running it, until you never see the error.The advantage of running the command with this suffix is, it only tries to get the missing files.So for the above command, if you see unable to fetch archives, simply run

This post is just for the people, who don't have any open source OS or Mac in their computer.For those, who have, you can skip this post.

Hey, let's come back to the installing openCV part in the next post.But first of all, let me tell you one thing, if you are on windows and are planning to learn openCV with the help of Visual Studio or Visual C++ bla bla bla, you will find yourself eventually in a very dangerous position.

I don't say windows is bad.Windows is one of the awesome softwares made in the previous century, but if you are planning to become a serious DEV, in todays world, you have to shift in to one of the Open Source Operating Systems.One more thing people say and a common rumour is that these OS's are not user friendly and they are very unstable.Neverthless, no open source is as robust as windows and the things you hear are partially true.But most of the supporting libraries for openCV and the openCV user community is not inclined to this and you need to install a lot of libraries that may make your system unstable some times.But, if you still plan to go for it, here's the link and don't forget to skip the rest of this post.

If you dare to take a step forward in your learning, and why I supported Windows in the above paragraph.....

What's ringing in your head is true, I want you to keep windows in your machine, till you get acquainted with the new OS or you will get bored of it (Either ways, you can go back to your old OS or gain some knowledge).

Personally, I recommend Ubuntu, and more preferrably install any version between 9.10 and 10.10.If you want to get started with Ubuntu, you can search for a free E-book "Ubuntu Pocket Guide", a relatively small one, that seriously gives you the confidence of a dev.

Ok, we will come back to our point, straight forwardly, I want you to dual boot your machine with Ubuntu.But if you are just a normal guy, who had no knowledge of installing OS, I recommend going with "Wubi" installer.The best thing about Wubi is, it lets you install Ubuntu just like any other Program in the control panel, and you can uninstall it with just a single click.More help about using Wubi, downloading and installing at the below link

One more serious thing to consider, while installing is that, it asks for you to install in some drive, try to make a seperate drive for it.If you don't know this, no problem, here's your step by step guide about creating a drive or volume or partition (all are same, as per windows terminology)

I am stressing this because even though you can allocate a space of some 10GB, in your drive with some 70 GB space, when you switch to Ubuntu, none of this 70 GB you can see, but you can see the other drives.

If you don't like experimenting, install Ubuntu in the smallest drive in your system or the drive that contains no important information.

While installing with Wubi, it restarts itself and when restarting, there will be two options, either windows or Ubuntu, make it go into Ubuntu to complete the installation part.After that when ever you start your system, you will have both Windows and Ubuntu for dual boot and if you want to uninstall, just go to control panel in Windows and check for Ubuntu and press uninstall.And one more thing, 15 GB is good enough space for Ubuntu and if you plan to give more, 20 GB is more than enough.In the next post, let's see about installing openCV

If you already know a programming language, you are halfway through, since it's only a library and mostly it provides single line functions, that make the most complex tasks easier.

I personally don't recommend any book, since I have never read any book and have no patience to read except novels :)

All the examples I provide will be related to C-language and you can easily get it and understand correlating with other languages.

I will start with image processing and take you slowly to the part where open cv can be used to perform Face Recognition. One thing I want to mention here is that there is no robust face recognising software in the market.Yes, it's the truth.The one I demonstrate, also will not be, but you can use your creativity...

And one more thing, don't read all the posts at a time, each day follow a post and go into it thoroughly.Yeah, now coming to the titled topic, this is the starting step, which will be in my next post ..

Open CV stands Open Computer Vision Library, created by Intel to shower their Processor power, but eventually became the tool for machine learning.

So why you need to learn open CV and what it will hold up to in your future ?

Open CV has become the defacto library for image processing and face recognition methods and there is a large community based support for open CV. It provides all the simple tools in the form of functions that make the most complex tasks in image processing easier.One more important things is that every one knows the future is "Robotics", and open CV provides you with the most exciting features to pursue your career in Computer vision.

Although it primarily supports C++, it's libraries are found in other languages like Java, Matlab etc.,